Formulation of Green Metro Train Service Plan Considering Passenger Travel Costs, Operational Costs, and Carbon Emissions

Grounded in the core principle of green transportation, this paper proposes a metro train service planning approach aimed at enhancing efficiency and reducing carbon emissions. The approach integrates environmental, passenger, and operator benefits, employing multiple train formations under full-length and short-turning route operations. Considering the high dimensionality of model variables and the complexity of the solution process, improvements are made to the neighborhood search strategy in the Adaptive Large-scale Neighborhood Search (ALNS) algorithm, and the improved algorithm is applied to the model solving process. Comprehensive data experiments are conducted to calibrate the algorithm parameters. Using Jinan Metro as a case study, the approach is empirically validated. The results demonstrate that, compared to the single-route and single-formation train service plans, the multi-route and multi-formation plan delivers superior performance in terms of carbon emissions, enterprise operating costs, and passenger travel time costs. Additionally, the Improved Adaptive Large-scale Neighborhood Search (IALNS) algorithm significantly outperforms the ALNS algorithm in both computational efficiency and solution quality. The main contribution of this paper is to balance the interests of both enterprises and passengers while effectively reducing carbon emissions. It also contributes to providing decision support for the green operation and sustainable development of metro systems.